Open Access

ARTICLE

Position Vectors Based Efficient Indoor Positioning System

Ayesha Javed¹, Mir Yasir Umair^1,*, Alina Mirza¹, Abdul Wakeel¹, Fazli Subhan², Wazir Zada Khan³

1 Department of Electrical Engineering, Military College of Signals, National University of Sciences and Technology, Islamabad, 44000, Pakistan
2 Department of Computer Engineering, National University of Modern Languages (NUML), Islamabad, 44000, Pakistan
3 Department of Computer Science and Information Security, Jazan University, Jazan, 45142, Saudi Arabia

* Corresponding Author: Mir Yasir Umair. Email:

(This article belongs to the Special Issue: Green IoT Networks using Machine Learning, Deep Learning Models)

Computers, Materials & Continua 2021, 67(2), 1781-1799. https://doi.org/10.32604/cmc.2021.015229

Received 01 November 2020; Accepted 26 November 2020; Issue published 05 February 2021

Abstract

With the advent and advancements in the wireless technologies, Wi-Fi fingerprinting-based Indoor Positioning System (IPS) has become one of the most promising solutions for localization in indoor environments. Unlike the outdoor environment, the lack of line-of-sight propagation in an indoor environment keeps the interest of the researchers to develop efficient and precise positioning systems that can later be incorporated in numerous applications involving Internet of Things (IoTs) and green computing. In this paper, we have proposed a technique that combines the capabilities of multiple algorithms to overcome the complexities experienced indoors. Initially, in the database development phase, Motley Kennan propagation model is used with Hough transformation to classify, detect, and assign different attenuation factors related to the types of walls. Furthermore, important parameters for system accuracy, such as, placement and geometry of Access Points (APs) in the coverage area are also considered. New algorithm for deployment of an additional AP to an already existing infrastructure is proposed by using Genetic Algorithm (GA) coupled with Enhanced Dilution of Precision (EDOP). Moreover, classification algorithm based on k-Nearest Neighbors (k-NN) is used to find the position of a stationary or mobile user inside the given coverage area. For k-NN to provide low localization error and reduced space dimensionality, three APs are required to be selected optimally. In this paper, we have suggested an idea to select APs based on Position Vectors (PV) as an input to the localization algorithm. Deducing from our comprehensive investigations, it is revealed that the accuracy of indoor positioning system using the proposed technique unblemished the existing solutions with significant improvements.

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Citations